{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/attribute-aware-non-linear-co-embeddings-of","title":"Attribute-aware non-linear co-embeddings of graph features","arxiv_id":null,"date":"2019-09-16","proceeding":"Thirteenth ACM Conference on Recommender Systems (RecSys ’19) 2019 9","authors":["Ahmed Rashed; Josif Grabocka; Lars Schmidt-Thieme"],"abstract":"In very sparse recommender data sets, attributes of users such as age, gender and home location and attributes of items such as, in the case of movies, genre, release year, and director can improve the recommendation accuracy, especially for users and items that have few ratings. While most recommendation models can be extended to take attributes of users and items into account, their architectures usually become more complicated. While attributes for items are often easy to be provided, attributes for users are often scarce for reasons of privacy or simply because they are not relevant to the operational process at hand. \r\nIn this paper, we address these two problems for attribute-aware recommender systems by proposing a simple model that co-embeds users and items into a joint latent space in a similar way as a vanilla matrix factorization, but with non-linear latent features construction that seamlessly can ingest user or item attributes or both (GraphRec). To address the second problem, scarce attributes, the proposed model treats the user-item relation as a bipartite graph and constructs generic user and item attributes via the Laplacian of the user-item co-occurrence graph that requires no further external side information but the mere rating matrix. In experiments on three recommender datasets, we show that GraphRec significantly outperforms existing state-of-the-art attribute-aware and content-aware recommender systems even without using any side information.","url_abs":"https://www.ismll.uni-hildesheim.de/pub/pdfs/Ahmed_RecSys19.pdf","url_pdf":"https://www.ismll.uni-hildesheim.de/pub/pdfs/Ahmed_RecSys19.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"attribute-aware-non-linear-co-embeddings-of","repo_url":"https://github.com/ahmedrashed-ml/GraphRec","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"attribute","task_name":"Attribute"},{"task_slug":"recommendation-systems","task_name":"Recommendation Systems"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/collaborative-filtering-on-movielens-100k","task":"Recommendation Systems","dataset":"MovieLens 100K","model":"GraphRec + Feat","rank_in_archive_order":5,"of":18,"metrics":{"RMSE (Random 90/10 Splits)":"0.883","RMSE (u1 Splits)":"0.897"},"uses_additional_data":true},{"leaderboard":"/sota/collaborative-filtering-on-movielens-100k","task":"Recommendation Systems","dataset":"MovieLens 100K","model":"GraphRec","rank_in_archive_order":6,"of":18,"metrics":{"RMSE (Random 90/10 Splits)":"0.887","RMSE (u1 Splits)":"0.904"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}